### Exam

#### Time and Place

**Time:**Dec 17 at 16.30 - 19.30 in AS2 / TUAS (check Oodi for possible updates)- There will be another (final) chance in late February. Follow Oodi for details.

#### Equipment

Only pens and simple calculators (nelilaskin) or function calculators (funktiolaskin) allowed. Books, notes, phones, computers, graphical calculators etc. are __not__ permitted.

#### Grading

Maximum number of points is 50. The minimum required for passing the course is 25. Grading emphasizes conceptual understanding over mathematical precision. Please note that equivocated answers -- i.e., fishing points by generating answers that are blatantly false or might in real life have damaging consequences -- will be penalized by deducing points.

#### Contents and learning objectives

The slides and assignments marked as Recommended are the primary material for the exam and sufficient for a passing grade. To aim for highest grades, we advise reading the additional papers listed in Materials.

Lecture 1: Combinatorial optimization: 1) Understanding of uses and assumptions of computational interaction and design; 2) Ability to cast simple design problems as combinatorial optimization tasks, including design space, objectives, constraints.

Lecture 2: Perception and attention: 1) Windows of visibility; 2) Rosenholtz' clutter model; 3) Ability to predict how bottom-up (saliency) and top-down attention would proceed for a given layout.

Lecture 3: Control: 1) Ability to predict movement with Fitts' law and steering law when parameters are given; 2) Ability to model (block diagram) a pointing gesture using control theory, in particular a block diagram implementing 2OL or similar model.

Lectures 4 and 5: Input: 1) Ability to tell what kinds of filtering are needed for different issues in raw sensor data; 2) Understanding of operating principles of a filter (e.g., 1€ filter) and a recognizer (e.g., 1$ recognizer). 3) Ability to construct a decoder for single or sequential input.

Lecture 6: Bayesian human-in-the-loop optimization: Understanding of core concepts in Bayesian optimization, including surrogate model, prior, update, acquisition function.

Lecture 7: Integer programming: Ability to formulate a menu and keyboard design problem as a mixed integer linear program.

Lecture 8: Biomechanics: Ability to evaluate the fatiguability of a given posture or movement using the Consumed Endurance model (when parameter values given).

Lecture 9: Formal methods: 1) Ability to draw a finite state diagram for simple interactive devices; 2) Ability to interpret a simple verification statement expressed with temporal logic (see slides).

Lecture 10: Cognitive models: Ability to formulate an information foraging diagram (patch model) for a given application case.

Lecture 11: Bandits: 1) Understanding of the bandit problem; 2) Understanding of how exploration/exploitation is solved; 3) Understanding of contextual bandits.

Lectures 12-13: Reinforcement learning: 1) Ability to formulate a navigation or decision-making task in interaction as a reinforcement learning problem, including the Markov decision process (MDP). 2) Understanding of difference between POMDP and MDP.

#### Format and task types

The exam will consist of 10 pages. Each page will contain one task worth of max. 5 points. The following task types may be used to test **general understanding**:

- Definition: E.g., define a concept in text or by a diagram.
- Explanation: E.g., explain a concept, model, or theory briefly in text or by a diagram.
- Assessment of a theory or model: E.g,. analyze pros and cons of a given theory, model, or concept.
- Short essay: E.g., provide an account of some phenomenon in interaction from a perspective coming from the course materials.

The following task types may be used to test the ability to apply knowledge to **practical problems**. In these problems:

- Analysis: E.g., given a design, analyze its different aspects from the perspective of a concept, model, or theory.
- Comparison: E.g., given two designs, analyze their pros and cons from the perspective of a concept, model, or theory.
- Numerical problem: E.g., given a design, identify the value of some property or outcome using a model.
- Re-design: E.g., given a design, propose a simple improvement by reference to a concept, theory, or model.
- Assessment of a design: E.g., given a design, analyze its pros and cons using appropriate models, concepts, or theories provided in the course. Assessment can be verbal or numerical.